Modeling, Identification and Control of Irrigation Station with Sprinkling: Takagi-Sugeno Approach - Complex System Modelling and Control Through Intelligent Soft Computations - page 485

Fig. 23 The decision surface of fuzzy controller

i i

S i ðÞ ¼ e ; min l

A ðÞ; l B

ðÞ ð 42 Þ

e ; U cfG ¼ max S i ð ðÞ Þ ð 43 Þ

The discourse universe of output U cf currency in ve fields (Fig. 23 )

ect a probably smooth law of order
what provides us an energy saving on the output of the fuzzy controller (Fig. 24 ).

The decision surface of fuzzy controller re fl

4.4 Simulation Results of Fuzzy Controller

The response of the fl flow if the system is regulated by the fuzzy controller exceeds

ow (8 m 3 the maximum h) accepted by the station of irrigation. One thus proposes

to add a saturation to compensate for this going beyond (Figs. 25 , 26 , 27 and, 28 ).

fl =

4.5 Comparative Study

Taking into account the results obtained, we note that for the two examples of
regulators, the fuzzy approach suggested makes it possible to obtain the best speed
ratio/energy of order however the fuzzy regulation brings a static error to the
evolution of the two outputs. The recourse has a pro it inserted into the exit makes it

possible to reduce this error (Fig. 29 , Tables 3 and 4 ).
The fuzzy controller ensures perfectly the control of the irrigation station by
sprinkling, on the other hand regulator PI used in the model appears robust from
point of view stabilization in transitory mode (Fig. 30 ).